Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory112.0 B

Variable types

Numeric13
Categorical1

Alerts

p1 is highly correlated with p2 and 2 other fieldsHigh correlation
p2 is highly correlated with p1High correlation
p3 is highly correlated with p1High correlation
p4 is highly correlated with p1High correlation
p1 is highly correlated with p2 and 2 other fieldsHigh correlation
p2 is highly correlated with p1High correlation
p3 is highly correlated with p1High correlation
p4 is highly correlated with p1High correlation
p1 is highly correlated with p2 and 2 other fieldsHigh correlation
p2 is highly correlated with p1High correlation
p3 is highly correlated with p1High correlation
p4 is highly correlated with p1High correlation
stab is highly correlated with stabfHigh correlation
stabf is highly correlated with stabHigh correlation
tau1 is uniformly distributed Uniform
tau2 is uniformly distributed Uniform
tau3 is uniformly distributed Uniform
tau4 is uniformly distributed Uniform
p2 is uniformly distributed Uniform
p3 is uniformly distributed Uniform
p4 is uniformly distributed Uniform
g1 is uniformly distributed Uniform
g2 is uniformly distributed Uniform
g3 is uniformly distributed Uniform
g4 is uniformly distributed Uniform
tau1 has unique values Unique
tau2 has unique values Unique
tau3 has unique values Unique
tau4 has unique values Unique
p1 has unique values Unique
p2 has unique values Unique
p3 has unique values Unique
p4 has unique values Unique
g1 has unique values Unique
g2 has unique values Unique
g3 has unique values Unique
g4 has unique values Unique
stab has unique values Unique

Reproduction

Analysis started2022-03-08 15:52:22.072767
Analysis finished2022-03-08 15:52:42.711266
Duration20.64 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

tau1
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.249999931
Minimum0.5007930214
Maximum9.999469469
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-03-08T21:22:42.954303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.5007930214
5-th percentile0.9756611516
Q12.874891569
median5.250003872
Q37.624689705
95-th percentile9.524566868
Maximum9.999469469
Range9.498676448
Interquartile range (IQR)4.749798136

Descriptive statistics

Standard deviation2.742548374
Coefficient of variation (CV)0.5223901734
Kurtosis-1.200002539
Mean5.249999931
Median Absolute Deviation (MAD)2.374981785
Skewness-5.411644484 × 10-6
Sum52499.99931
Variance7.521571584
MonotonicityNot monotonic
2022-03-08T21:22:43.059325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.9590600251
 
< 0.1%
4.9281467251
 
< 0.1%
8.942265671
 
< 0.1%
9.7390264911
 
< 0.1%
5.5938260031
 
< 0.1%
4.7456880891
 
< 0.1%
1.2612572491
 
< 0.1%
8.5591920651
 
< 0.1%
7.5812595411
 
< 0.1%
6.5896164231
 
< 0.1%
Other values (9990)9990
99.9%
ValueCountFrequency (%)
0.50079302141
< 0.1%
0.50133076081
< 0.1%
0.5023206291
< 0.1%
0.50328379781
< 0.1%
0.50401426171
< 0.1%
0.50496005031
< 0.1%
0.50641890811
< 0.1%
0.50673596351
< 0.1%
0.50853320541
< 0.1%
0.50912399771
< 0.1%
ValueCountFrequency (%)
9.9994694691
< 0.1%
9.9989945741
< 0.1%
9.9977381271
< 0.1%
9.996677921
< 0.1%
9.9953752111
< 0.1%
9.994355531
< 0.1%
9.9939606771
< 0.1%
9.993045921
< 0.1%
9.9917979971
< 0.1%
9.9910463951
< 0.1%

tau2
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.250001022
Minimum0.5001413605
Maximum9.999836556
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-03-08T21:22:43.166349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.5001413605
5-th percentile0.9751461276
Q12.875140007
median5.249981261
Q37.624893125
95-th percentile9.524364309
Maximum9.999836556
Range9.499695196
Interquartile range (IQR)4.749753117

Descriptive statistics

Standard deviation2.742548674
Coefficient of variation (CV)0.5223901219
Kurtosis-1.200000598
Mean5.250001022
Median Absolute Deviation (MAD)2.374934178
Skewness2.740300294 × 10-6
Sum52500.01022
Variance7.521573228
MonotonicityNot monotonic
2022-03-08T21:22:43.270387image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.0798852041
 
< 0.1%
5.401949261
 
< 0.1%
3.235409751
 
< 0.1%
8.4861528671
 
< 0.1%
8.0716958381
 
< 0.1%
4.1570543151
 
< 0.1%
4.7927439041
 
< 0.1%
8.3865203591
 
< 0.1%
4.0612628961
 
< 0.1%
7.437670531
 
< 0.1%
Other values (9990)9990
99.9%
ValueCountFrequency (%)
0.50014136051
< 0.1%
0.50158164191
< 0.1%
0.50204869691
< 0.1%
0.50308447441
< 0.1%
0.5041281811
< 0.1%
0.50537150991
< 0.1%
0.50583367471
< 0.1%
0.50724386941
< 0.1%
0.50784585181
< 0.1%
0.50864474981
< 0.1%
ValueCountFrequency (%)
9.9998365561
< 0.1%
9.9982912361
< 0.1%
9.9979071311
< 0.1%
9.9968019881
< 0.1%
9.9958363731
< 0.1%
9.9949884221
< 0.1%
9.9942634371
< 0.1%
9.9932128451
< 0.1%
9.9922435781
< 0.1%
9.9906063181
< 0.1%

tau3
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.250003524
Minimum0.5007881527
Maximum9.999450008
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-03-08T21:22:43.376416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.5007881527
5-th percentile0.9752198009
Q12.875521684
median5.249978587
Q37.624948268
95-th percentile9.52417191
Maximum9.999450008
Range9.498661856
Interquartile range (IQR)4.749426584

Descriptive statistics

Standard deviation2.742549458
Coefficient of variation (CV)0.5223900222
Kurtosis-1.200004417
Mean5.250003524
Median Absolute Deviation (MAD)2.37499528
Skewness-4.799959239 × 10-6
Sum52500.03524
Variance7.521577527
MonotonicityNot monotonic
2022-03-08T21:22:43.482421image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.3810253921
 
< 0.1%
4.5766575071
 
< 0.1%
8.1371902951
 
< 0.1%
7.8162011331
 
< 0.1%
5.3472660081
 
< 0.1%
7.8559942481
 
< 0.1%
1.5183364741
 
< 0.1%
4.130771771
 
< 0.1%
6.1926411551
 
< 0.1%
1.9800746381
 
< 0.1%
Other values (9990)9990
99.9%
ValueCountFrequency (%)
0.50078815271
< 0.1%
0.50165767361
< 0.1%
0.5023761931
< 0.1%
0.50348186441
< 0.1%
0.50443084751
< 0.1%
0.50514379321
< 0.1%
0.50648161721
< 0.1%
0.50757334681
< 0.1%
0.50803529191
< 0.1%
0.5093716551
< 0.1%
ValueCountFrequency (%)
9.9994500081
< 0.1%
9.9981139261
< 0.1%
9.9974276451
< 0.1%
9.9962633021
< 0.1%
9.9955953481
< 0.1%
9.9946958141
< 0.1%
9.9941247951
< 0.1%
9.9929819441
< 0.1%
9.9920730471
< 0.1%
9.9910037951
< 0.1%

tau4
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.249997064
Minimum0.5004729612
Maximum9.999443297
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-03-08T21:22:43.588464image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.5004729612
5-th percentile0.9752191253
Q12.874950181
median5.249734121
Q37.62483773
95-th percentile9.524199832
Maximum9.999443297
Range9.498970336
Interquartile range (IQR)4.749887549

Descriptive statistics

Standard deviation2.74255553
Coefficient of variation (CV)0.5223918216
Kurtosis-1.199998806
Mean5.249997064
Median Absolute Deviation (MAD)2.375011023
Skewness-1.409045706 × 10-6
Sum52499.97064
Variance7.521610835
MonotonicityNot monotonic
2022-03-08T21:22:43.693487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.7807544321
 
< 0.1%
1.72143661
 
< 0.1%
7.0458643461
 
< 0.1%
9.3807783071
 
< 0.1%
6.0720631521
 
< 0.1%
7.4785506991
 
< 0.1%
4.055179981
 
< 0.1%
4.2213872641
 
< 0.1%
6.5572688871
 
< 0.1%
3.1980119621
 
< 0.1%
Other values (9990)9990
99.9%
ValueCountFrequency (%)
0.50047296121
< 0.1%
0.50157121281
< 0.1%
0.50202458771
< 0.1%
0.50287867531
< 0.1%
0.50401562751
< 0.1%
0.50482698871
< 0.1%
0.50610993031
< 0.1%
0.50724445181
< 0.1%
0.50806691371
< 0.1%
0.50922595171
< 0.1%
ValueCountFrequency (%)
9.9994432971
< 0.1%
9.9989122091
< 0.1%
9.9974482661
< 0.1%
9.9963150091
< 0.1%
9.9953801781
< 0.1%
9.9948295271
< 0.1%
9.9942193341
< 0.1%
9.9931038491
< 0.1%
9.9922811731
< 0.1%
9.9905013261
< 0.1%

p1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.749999958
Minimum1.582589665
Maximum5.86441796
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-03-08T21:22:43.800511image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.582589665
5-th percentile2.501846406
Q13.218299791
median3.751025437
Q34.282420205
95-th percentile4.988739885
Maximum5.86441796
Range4.281828295
Interquartile range (IQR)1.064120414

Descriptive statistics

Standard deviation0.7521601049
Coefficient of variation (CV)0.2005760302
Kurtosis-0.3894686574
Mean3.749999958
Median Absolute Deviation (MAD)0.532142361
Skewness-0.01269000964
Sum37499.99958
Variance0.5657448234
MonotonicityNot monotonic
2022-03-08T21:22:43.898533image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.7630847721
 
< 0.1%
4.7706502411
 
< 0.1%
4.2475264691
 
< 0.1%
3.8706931051
 
< 0.1%
2.8432626261
 
< 0.1%
2.8480588721
 
< 0.1%
4.3688867891
 
< 0.1%
2.4163704211
 
< 0.1%
3.2211929571
 
< 0.1%
4.918483941
 
< 0.1%
Other values (9990)9990
99.9%
ValueCountFrequency (%)
1.5825896651
< 0.1%
1.642859641
< 0.1%
1.6756333171
< 0.1%
1.6802212351
< 0.1%
1.6873562651
< 0.1%
1.6955755121
< 0.1%
1.7189190911
< 0.1%
1.722836751
< 0.1%
1.7649107831
< 0.1%
1.7852559251
< 0.1%
ValueCountFrequency (%)
5.864417961
< 0.1%
5.8272845011
< 0.1%
5.8187054721
< 0.1%
5.8141286321
< 0.1%
5.7982731371
< 0.1%
5.7841415181
< 0.1%
5.7527060421
< 0.1%
5.7503336441
< 0.1%
5.7332048461
< 0.1%
5.7325332261
< 0.1%

p2
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.250000457
Minimum-1.999890956
Maximum-0.5001082759
Zeros0
Zeros (%)0.0%
Negative10000
Negative (%)100.0%
Memory size78.2 KiB
2022-03-08T21:22:43.999556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1.999890956
5-th percentile-1.924874323
Q1-1.624901492
median-1.249966291
Q3-0.8749770862
95-th percentile-0.5751312907
Maximum-0.5001082759
Range1.49978268
Interquartile range (IQR)0.7499244054

Descriptive statistics

Standard deviation0.4330348499
Coefficient of variation (CV)-0.3464277532
Kurtosis-1.200001636
Mean-1.250000457
Median Absolute Deviation (MAD)0.3750080111
Skewness4.145819947 × 10-6
Sum-12500.00457
Variance0.1875191813
MonotonicityNot monotonic
2022-03-08T21:22:44.111581image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.7826036311
 
< 0.1%
-1.3952067921
 
< 0.1%
-0.6635661991
 
< 0.1%
-1.4270609781
 
< 0.1%
-0.99953687661
 
< 0.1%
-1.582640731
 
< 0.1%
-1.9806083781
 
< 0.1%
-1.1435067941
 
< 0.1%
-1.4335618911
 
< 0.1%
-1.2645575821
 
< 0.1%
Other values (9990)9990
99.9%
ValueCountFrequency (%)
-1.9998909561
< 0.1%
-1.9997413941
< 0.1%
-1.9996551261
< 0.1%
-1.9995056571
< 0.1%
-1.9993707081
< 0.1%
-1.9991843481
< 0.1%
-1.998995191
< 0.1%
-1.9988574421
< 0.1%
-1.9986544661
< 0.1%
-1.9985065871
< 0.1%
ValueCountFrequency (%)
-0.50010827591
< 0.1%
-0.50027406341
< 0.1%
-0.50031418191
< 0.1%
-0.50046037391
< 0.1%
-0.50061040911
< 0.1%
-0.50078054151
< 0.1%
-0.50091497021
< 0.1%
-0.50109849591
< 0.1%
-0.50123606311
< 0.1%
-0.50143039061
< 0.1%

p3
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.249999744
Minimum-1.999944669
Maximum-0.5000722515
Zeros0
Zeros (%)0.0%
Negative10000
Negative (%)100.0%
Memory size78.2 KiB
2022-03-08T21:22:44.224607image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1.999944669
5-th percentile-1.924888465
Q1-1.6250253
median-1.249974308
Q3-0.8750431173
95-th percentile-0.5751058978
Maximum-0.5000722515
Range1.499872418
Interquartile range (IQR)0.7499821827

Descriptive statistics

Standard deviation0.4330350963
Coefficient of variation (CV)-0.3464281479
Kurtosis-1.200002566
Mean-1.249999744
Median Absolute Deviation (MAD)0.3750469493
Skewness4.459360587 × 10-6
Sum-12499.99744
Variance0.1875193947
MonotonicityNot monotonic
2022-03-08T21:22:44.331631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.257394831
 
< 0.1%
-1.6462109851
 
< 0.1%
-1.98684941
 
< 0.1%
-0.59656875991
 
< 0.1%
-0.93349031681
 
< 0.1%
-0.66941681021
 
< 0.1%
-1.3921661161
 
< 0.1%
-0.71198501981
 
< 0.1%
-0.59107143481
 
< 0.1%
-1.7926526831
 
< 0.1%
Other values (9990)9990
99.9%
ValueCountFrequency (%)
-1.9999446691
< 0.1%
-1.9997147351
< 0.1%
-1.9996078231
< 0.1%
-1.9994567351
< 0.1%
-1.9992989151
< 0.1%
-1.9991592291
< 0.1%
-1.9990340751
< 0.1%
-1.9989405061
< 0.1%
-1.9987203891
< 0.1%
-1.9985962281
< 0.1%
ValueCountFrequency (%)
-0.50007225151
< 0.1%
-0.5002896231
< 0.1%
-0.50036053971
< 0.1%
-0.50057513691
< 0.1%
-0.50068172411
< 0.1%
-0.50084279051
< 0.1%
-0.50091848361
< 0.1%
-0.50109243941
< 0.1%
-0.50126592051
< 0.1%
-0.50144806571
< 0.1%

p4
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.249999756
Minimum-1.999926332
Maximum-0.5000245287
Zeros0
Zeros (%)0.0%
Negative10000
Negative (%)100.0%
Memory size78.2 KiB
2022-03-08T21:22:44.443655image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1.999926332
5-th percentile-1.924926997
Q1-1.624959539
median-1.250007275
Q3-0.8750646635
95-th percentile-0.5750383946
Maximum-0.5000245287
Range1.499901803
Interquartile range (IQR)0.7498948751

Descriptive statistics

Standard deviation0.4330349942
Coefficient of variation (CV)-0.3464280629
Kurtosis-1.199995596
Mean-1.249999756
Median Absolute Deviation (MAD)0.3749751502
Skewness9.157719434 × 10-7
Sum-12499.99756
Variance0.1875193062
MonotonicityNot monotonic
2022-03-08T21:22:44.550680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.7230863111
 
< 0.1%
-1.7292324651
 
< 0.1%
-1.597110871
 
< 0.1%
-1.8470633671
 
< 0.1%
-0.91023543251
 
< 0.1%
-0.59600133211
 
< 0.1%
-0.99611229411
 
< 0.1%
-0.56087860721
 
< 0.1%
-1.1965596311
 
< 0.1%
-1.8612736751
 
< 0.1%
Other values (9990)9990
99.9%
ValueCountFrequency (%)
-1.9999263321
< 0.1%
-1.999786881
< 0.1%
-1.9996751011
< 0.1%
-1.9994491971
< 0.1%
-1.9993358941
< 0.1%
-1.999229291
< 0.1%
-1.9990756171
< 0.1%
-1.9989106741
< 0.1%
-1.9986599561
< 0.1%
-1.9985774311
< 0.1%
ValueCountFrequency (%)
-0.50002452871
< 0.1%
-0.50019828331
< 0.1%
-0.50042564271
< 0.1%
-0.5005923631
< 0.1%
-0.50069225171
< 0.1%
-0.50081126381
< 0.1%
-0.50093724481
< 0.1%
-0.50114409221
< 0.1%
-0.50121485331
< 0.1%
-0.50148446031
< 0.1%

g1
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5249997888
Minimum0.05000930361
Maximum0.9999370731
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-03-08T21:22:44.809738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.05000930361
5-th percentile0.09752615419
Q10.2875213775
median0.5250091478
Q30.762434854
95-th percentile0.9524950543
Maximum0.9999370731
Range0.9499277695
Interquartile range (IQR)0.4749134765

Descriptive statistics

Standard deviation0.274255532
Coefficient of variation (CV)0.5223916995
Kurtosis-1.199998588
Mean0.5249997888
Median Absolute Deviation (MAD)0.2374818078
Skewness2.956457786 × 10-6
Sum5249.997888
Variance0.07521609681
MonotonicityNot monotonic
2022-03-08T21:22:44.915761image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.65045646091
 
< 0.1%
0.69095830221
 
< 0.1%
0.81286207611
 
< 0.1%
0.17923028811
 
< 0.1%
0.62669416321
 
< 0.1%
0.64843600331
 
< 0.1%
0.10404468611
 
< 0.1%
0.92780567581
 
< 0.1%
0.21707595361
 
< 0.1%
0.70008687481
 
< 0.1%
Other values (9990)9990
99.9%
ValueCountFrequency (%)
0.050009303611
< 0.1%
0.050184193451
< 0.1%
0.050211921741
< 0.1%
0.050324037211
< 0.1%
0.050389782311
< 0.1%
0.050528840191
< 0.1%
0.050591784581
< 0.1%
0.050727537891
< 0.1%
0.050824068991
< 0.1%
0.050888165811
< 0.1%
ValueCountFrequency (%)
0.99993707311
< 0.1%
0.99984601521
< 0.1%
0.99980599381
< 0.1%
0.99962804871
< 0.1%
0.99959995511
< 0.1%
0.99946665711
< 0.1%
0.99935715351
< 0.1%
0.99929498421
< 0.1%
0.99917538411
< 0.1%
0.99912514231
< 0.1%

g2
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5250002584
Minimum0.05005310104
Maximum0.9999442945
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-03-08T21:22:45.021785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.05005310104
5-th percentile0.09750052446
Q10.2875516355
median0.525003147
Q30.7624903124
95-th percentile0.9525000106
Maximum0.9999442945
Range0.9498911935
Interquartile range (IQR)0.4749386769

Descriptive statistics

Standard deviation0.2742549034
Coefficient of variation (CV)0.5223900352
Kurtosis-1.200001589
Mean0.5250002584
Median Absolute Deviation (MAD)0.2374912614
Skewness-6.13746225 × 10-7
Sum5250.002584
Variance0.07521575206
MonotonicityNot monotonic
2022-03-08T21:22:45.132810image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.85957810581
 
< 0.1%
0.52825103511
 
< 0.1%
0.92956900591
 
< 0.1%
0.40192813421
 
< 0.1%
0.11295273981
 
< 0.1%
0.59492976061
 
< 0.1%
0.054406473381
 
< 0.1%
0.8052749321
 
< 0.1%
0.68061846181
 
< 0.1%
0.56276937231
 
< 0.1%
Other values (9990)9990
99.9%
ValueCountFrequency (%)
0.050053101041
< 0.1%
0.050129782951
< 0.1%
0.050273638831
< 0.1%
0.050296364371
< 0.1%
0.05038564751
< 0.1%
0.050488645361
< 0.1%
0.050641799571
< 0.1%
0.050708348531
< 0.1%
0.050784307311
< 0.1%
0.050907937441
< 0.1%
ValueCountFrequency (%)
0.99994429451
< 0.1%
0.99989348631
< 0.1%
0.99973909821
< 0.1%
0.99969662241
< 0.1%
0.99956812791
< 0.1%
0.99948420961
< 0.1%
0.99934176561
< 0.1%
0.99930934591
< 0.1%
0.99920305191
< 0.1%
0.99906883281
< 0.1%

g3
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5250003398
Minimum0.05005369237
Maximum0.9999818324
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-03-08T21:22:45.238834image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.05005369237
5-th percentile0.09751699997
Q10.2875136427
median0.5250146933
Q30.7624402157
95-th percentile0.9524856763
Maximum0.9999818324
Range0.94992814
Interquartile range (IQR)0.474926573

Descriptive statistics

Standard deviation0.274254813
Coefficient of variation (CV)0.522389782
Kurtosis-1.199993471
Mean0.5250003398
Median Absolute Deviation (MAD)0.237482785
Skewness3.204325951 × 10-6
Sum5250.003398
Variance0.07521570248
MonotonicityNot monotonic
2022-03-08T21:22:45.345846image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.88744492061
 
< 0.1%
0.14801388081
 
< 0.1%
0.070145450991
 
< 0.1%
0.84309221521
 
< 0.1%
0.051026578021
 
< 0.1%
0.37502690141
 
< 0.1%
0.66989541331
 
< 0.1%
0.83799389861
 
< 0.1%
0.45729494451
 
< 0.1%
0.82793267621
 
< 0.1%
Other values (9990)9990
99.9%
ValueCountFrequency (%)
0.050053692371
< 0.1%
0.050103888171
< 0.1%
0.050270427241
< 0.1%
0.050376670241
< 0.1%
0.05046484591
< 0.1%
0.050508591031
< 0.1%
0.050624737841
< 0.1%
0.050759084751
< 0.1%
0.050789456831
< 0.1%
0.050921353421
< 0.1%
ValueCountFrequency (%)
0.99998183241
< 0.1%
0.99983945671
< 0.1%
0.99974482291
< 0.1%
0.99965399531
< 0.1%
0.99958101131
< 0.1%
0.99943697211
< 0.1%
0.99933618381
< 0.1%
0.99927901841
< 0.1%
0.99917115721
< 0.1%
0.99912684931
< 0.1%

g4
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5250000906
Minimum0.05002849396
Maximum0.999930048
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-03-08T21:22:45.451882image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.05002849396
5-th percentile0.0975854354
Q10.287494443
median0.5250016344
Q30.7624329891
95-th percentile0.9524880226
Maximum0.999930048
Range0.949901554
Interquartile range (IQR)0.4749385461

Descriptive statistics

Standard deviation0.2742548447
Coefficient of variation (CV)0.5223900901
Kurtosis-1.200002792
Mean0.5250000906
Median Absolute Deviation (MAD)0.2374966921
Skewness3.871023655 × 10-6
Sum5250.000906
Variance0.07521571982
MonotonicityNot monotonic
2022-03-08T21:22:45.557906image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.95803398761
 
< 0.1%
0.35495188611
 
< 0.1%
0.77397537131
 
< 0.1%
0.62229072011
 
< 0.1%
0.88623564711
 
< 0.1%
0.76627664421
 
< 0.1%
0.25051881531
 
< 0.1%
0.59411716771
 
< 0.1%
0.72629868511
 
< 0.1%
0.15038385191
 
< 0.1%
Other values (9990)9990
99.9%
ValueCountFrequency (%)
0.050028493961
< 0.1%
0.050170232381
< 0.1%
0.050231089821
< 0.1%
0.050335700141
< 0.1%
0.050457946791
< 0.1%
0.050546399771
< 0.1%
0.050632864431
< 0.1%
0.050730920971
< 0.1%
0.050794743071
< 0.1%
0.050922751621
< 0.1%
ValueCountFrequency (%)
0.9999300481
< 0.1%
0.99988162671
< 0.1%
0.9997388041
< 0.1%
0.99964008271
< 0.1%
0.99957572711
< 0.1%
0.99945513481
< 0.1%
0.99940944981
< 0.1%
0.99932800621
< 0.1%
0.99918510921
< 0.1%
0.9990787771
< 0.1%

stab
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0157309
Minimum-0.08075989242
Maximum0.1094032063
Zeros0
Zeros (%)0.0%
Negative3620
Negative (%)36.2%
Memory size78.2 KiB
2022-03-08T21:22:45.664924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-0.08075989242
5-th percentile-0.04218157397
Q1-0.0155569066
median0.01714170443
Q30.04487844087
95-th percentile0.07474149733
Maximum0.1094032063
Range0.1901630987
Interquartile range (IQR)0.06043534747

Descriptive statistics

Standard deviation0.03691903237
Coefficient of variation (CV)2.346911643
Kurtosis-0.9117333228
Mean0.0157309
Median Absolute Deviation (MAD)0.02995865001
Skewness0.01866344185
Sum157.309
Variance0.001363014951
MonotonicityNot monotonic
2022-03-08T21:22:45.771949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.055347489171
 
< 0.1%
0.0077787731161
 
< 0.1%
0.05327095631
 
< 0.1%
0.060621544061
 
< 0.1%
0.034466462171
 
< 0.1%
0.06086434741
 
< 0.1%
-0.046629276341
 
< 0.1%
0.080442295551
 
< 0.1%
0.050475322871
 
< 0.1%
0.018692116791
 
< 0.1%
Other values (9990)9990
99.9%
ValueCountFrequency (%)
-0.080759892421
< 0.1%
-0.074923277291
< 0.1%
-0.07052885661
< 0.1%
-0.070483609681
< 0.1%
-0.069500702681
< 0.1%
-0.06870873431
< 0.1%
-0.068062170641
< 0.1%
-0.067493825071
< 0.1%
-0.066556511591
< 0.1%
-0.066400915111
< 0.1%
ValueCountFrequency (%)
0.10940320631
< 0.1%
0.10897064821
< 0.1%
0.10738006781
< 0.1%
0.10727234631
< 0.1%
0.10706022021
< 0.1%
0.10696159691
< 0.1%
0.10680296471
< 0.1%
0.10645144591
< 0.1%
0.10612172141
< 0.1%
0.10570218871
< 0.1%

stabf
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
unstable
6380 
stable
3620 

Length

Max length8
Median length8
Mean length7.276
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowunstable
2nd rowstable
3rd rowunstable
4th rowunstable
5th rowunstable

Common Values

ValueCountFrequency (%)
unstable6380
63.8%
stable3620
36.2%

Length

2022-03-08T21:22:45.877978image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-08T21:22:45.937991image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
unstable6380
63.8%
stable3620
36.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-03-08T21:22:41.091777image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:26.071840image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:27.268099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:28.430362image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:29.729647image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:30.883907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:32.210735image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:33.504072image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:34.905400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:36.152685image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:37.452972image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:38.611237image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:39.770485image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:41.184813image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:26.169862image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:27.354113image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:28.518394image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:29.814666image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:30.969449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:32.310254image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:33.597100image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:34.997414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:36.237686image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:37.538994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:38.697252image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:40.006531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:41.276823image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:26.253881image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:27.439152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:28.746440image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:29.900686image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:31.053466image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:32.403831image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:33.691114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:35.089446image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:36.321718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:37.626001image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:38.783270image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:40.092549image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:41.370856image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:26.339886image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:27.524171image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:28.830444image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:29.985704image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:31.139486image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:32.514863image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:33.784148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:35.181463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:36.409732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:37.713030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:38.869275image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:40.177570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:41.467873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:26.424906image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:27.610185image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:28.913464image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:30.071726image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:31.223505image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:32.604884image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:33.877169image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:35.272482image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:36.494759image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:37.799050image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:38.955314image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:40.265588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:41.562896image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:26.511924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:27.695206image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:28.998486image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:30.157743image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:31.309531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:32.704902image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:33.970190image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:35.364488image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:36.580776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:37.883062image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:39.041328image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:40.360625image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:41.664919image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:26.607945image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:27.792216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:29.095504image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:30.253765image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:31.551578image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:32.806928image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:34.071218image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:35.465525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:36.675791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:37.978075image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:39.134354image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:40.457639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:41.766945image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:26.704967image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:27.887233image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:29.194541image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:30.346799image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:31.652616image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:32.910939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:34.174224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:35.575538image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:36.770806image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:38.073097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:39.238378image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:40.553656image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:41.868968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:26.801989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:27.982254image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:29.288567image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:30.441818image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:31.750638image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:33.013971image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:34.428296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:35.675566image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:36.866845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:38.170119image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:39.332398image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:40.649690image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:41.965990image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:26.894010image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:28.070275image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:29.374582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:30.526839image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:31.839659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:33.106981image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:34.522299image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:35.769594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:36.952864image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:38.256157image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:39.418418image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:40.736698image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:42.062007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:26.996041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:28.160308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:29.461586image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:30.617847image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:31.930683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:33.201004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:34.616340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:35.861608image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:37.182916image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:38.343158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:39.502437image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:40.824719image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:42.155028image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:27.085073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:28.246329image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:29.547607image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:30.702870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:32.022699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:33.296024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:34.708340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:35.953635image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:37.269931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:38.429196image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:39.587456image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:40.910748image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:42.249041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:27.170092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:28.334348image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:29.633627image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:30.788884image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:32.112719image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:33.393045image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:34.802363image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:36.046649image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:37.354951image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:38.516204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:39.673475image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T21:22:40.995767image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-03-08T21:22:45.999005image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-03-08T21:22:46.176025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-03-08T21:22:46.315056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-03-08T21:22:46.451088image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-03-08T21:22:42.431089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-03-08T21:22:42.625135image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

tau1tau2tau3tau4p1p2p3p4g1g2g3g4stabstabf
02.9590603.0798858.3810259.7807543.763085-0.782604-1.257395-1.7230860.6504560.8595780.8874450.9580340.055347unstable
19.3040974.9025243.0475411.3693575.067812-1.940058-1.872742-1.2550120.4134410.8624140.5621390.781760-0.005957stable
28.9717078.8484283.0464791.2145183.405158-1.207456-1.277210-0.9204920.1630410.7666890.8394440.1098530.003471unstable
30.7164157.6696004.4866412.3405633.963791-1.027473-1.938944-0.9973740.4462090.9767440.9293810.3627180.028871unstable
43.1341127.6087724.9437599.8575733.525811-1.125531-1.845975-0.5543050.7971100.4554500.6569470.8209230.049860unstable
56.9992099.1092473.7840664.2677884.429669-1.857139-0.670397-1.9021330.2617930.0779300.5428840.469931-0.017385stable
66.7101663.7652046.9293148.8185622.397419-0.614590-1.208826-0.5740040.1778900.3979770.4020460.3766300.005954unstable
76.9535121.3791255.7194007.8703073.224495-0.748998-1.186517-1.2889800.3713850.6332040.7327410.3805440.016634unstable
84.6898524.0077471.4785733.7337874.041300-1.410344-1.238204-1.3927510.2697080.2503640.1649410.482439-0.038677stable
99.8414961.4138229.7698567.6416164.727595-1.991363-0.857637-1.8785940.3763560.5444150.7920390.1162630.012383unstable

Last rows

tau1tau2tau3tau4p1p2p3p4g1g2g3g4stabstabf
99905.7832994.7266141.3402738.6179334.587533-1.950574-1.594137-1.0428220.4458530.6456800.4068640.7261800.028673unstable
99910.9989889.9249168.9265632.8859413.660232-1.103521-1.105641-1.4510700.7176600.9549190.4911070.6920230.008260unstable
99923.1144424.7810722.4279187.9895092.673156-0.918191-0.652736-1.1022280.8679500.8888580.4605870.9650260.064645unstable
99935.7541913.0327435.0848034.6336245.199250-1.717030-1.713212-1.7690090.1572840.9759210.5115550.6965910.050212unstable
99942.0429548.5143358.1738095.4666353.783797-1.639912-0.662469-1.4814170.1541290.9444860.0532250.4991090.026311unstable
99952.9304069.4876272.3765236.1877973.343416-0.658054-1.449106-1.2362560.6017090.7796420.8135120.6083850.023892unstable
99963.3922991.2748272.9549476.8947594.349512-1.663661-0.952437-1.7334140.5020790.5672420.2858800.366120-0.025803stable
99972.3640342.8420308.7763911.0089064.299976-1.380719-0.943884-1.9753730.4878380.9865050.1492860.145984-0.031810stable
99989.6315113.9943982.7570717.8213472.514755-0.966330-0.649915-0.8985100.3652460.5875580.8891180.8183910.037789unstable
99996.5305276.7817904.3496958.6731383.492807-1.390285-1.532193-0.5703290.0730560.5054410.3787610.9426310.045263unstable